{"id":"https://openalex.org/W7123357371","doi":"https://doi.org/10.1109/tmm.2026.3651117","title":"Posture-Movement-Frequency-Enhanced Graph Convolutional Network for Gait Emotion Recognition","display_name":"Posture-Movement-Frequency-Enhanced Graph Convolutional Network for Gait Emotion Recognition","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7123357371","doi":"https://doi.org/10.1109/tmm.2026.3651117"},"language":null,"primary_location":{"id":"doi:10.1109/tmm.2026.3651117","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2026.3651117","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021971414","display_name":"Faliang Huang","orcid":"https://orcid.org/0000-0002-0656-7361"},"institutions":[{"id":"https://openalex.org/I4210151929","display_name":"Nanning Normal University","ror":"https://ror.org/04dx82x73","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210151929"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Faliang Huang","raw_affiliation_strings":["Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, China"],"raw_orcid":"https://orcid.org/0000-0002-0656-7361","affiliations":[{"raw_affiliation_string":"Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, China","institution_ids":["https://openalex.org/I4210151929"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058454592","display_name":"Jiayi Yao","orcid":"https://orcid.org/0000-0002-8588-4356"},"institutions":[{"id":"https://openalex.org/I4210151929","display_name":"Nanning Normal University","ror":"https://ror.org/04dx82x73","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210151929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayi Yao","raw_affiliation_strings":["Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, China"],"raw_orcid":"https://orcid.org/0009-0003-6866-6917","affiliations":[{"raw_affiliation_string":"Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, China","institution_ids":["https://openalex.org/I4210151929"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122904921","display_name":"Jiachun Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I4210151929","display_name":"Nanning Normal University","ror":"https://ror.org/04dx82x73","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210151929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiachun Xie","raw_affiliation_strings":["Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, China"],"raw_orcid":"https://orcid.org/0009-0006-7954-6904","affiliations":[{"raw_affiliation_string":"Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, China","institution_ids":["https://openalex.org/I4210151929"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012858728","display_name":"Yihua Ye","orcid":"https://orcid.org/0000-0002-2281-0085"},"institutions":[{"id":"https://openalex.org/I4210151929","display_name":"Nanning Normal University","ror":"https://ror.org/04dx82x73","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210151929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihua Ye","raw_affiliation_strings":["Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, China","institution_ids":["https://openalex.org/I4210151929"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122855710","display_name":"Yuping Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210151929","display_name":"Nanning Normal University","ror":"https://ror.org/04dx82x73","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210151929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuping Chen","raw_affiliation_strings":["Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, China","institution_ids":["https://openalex.org/I4210151929"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5021971414"],"corresponding_institution_ids":["https://openalex.org/I4210151929"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06371648,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":null,"first_page":"3270","last_page":"3283"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.5541999936103821,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.5541999936103821,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.3767000138759613,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.01720000058412552,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6237000226974487},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.609000027179718},{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.5102999806404114},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5044000148773193},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4374000132083893},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.4361000061035156},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3617999851703644}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8217999935150146},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6237000226974487},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.609000027179718},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5893999934196472},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.5102999806404114},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5044000148773193},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4374000132083893},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.4361000061035156},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3617999851703644},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3513999879360199},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.34049999713897705},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.2851000130176544},{"id":"https://openalex.org/C6438553","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Affective computing","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2768999934196472},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2711000144481659},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2026.3651117","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2026.3651117","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.4634152352809906}],"awards":[{"id":"https://openalex.org/G5389225855","display_name":null,"funder_award_id":"62262045","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"progress":[1],"in":[2,48,72,101,169],"recognizing":[3],"emotions":[4],"through":[5,146],"gait":[6,23,102,170],"analysis":[7],"has":[8],"attracted":[9],"substantial":[10],"interest.":[11],"Spatial":[12],"temporal":[13,74,147],"graph":[14,85],"convolutional":[15],"networks":[16],"(ST-GCN)":[17],"have":[18],"been":[19],"applied":[20],"to":[21,44,60,119],"extract":[22],"features":[24,109,143],"effectively,":[25],"enabling":[26],"enhanced":[27],"emotion":[28,54,103,121,142,171],"recognition.":[29],"However,":[30],"existing":[31],"methods":[32,58],"do":[33],"not":[34],"account":[35],"for":[36,97],"subtle":[37],"movement":[38,108],"cues":[39],"that":[40,164],"are":[41],"intricately":[42],"linked":[43],"human":[45],"emotions,":[46],"resulting":[47],"a":[49,129],"lack":[50],"of":[51,53,65,107,140],"representation":[52],"intensity.":[55],"Additionally,":[56],"these":[57,78],"fail":[59],"consider":[61],"the":[62,73,98,105],"cyclic":[63],"nature":[64],"gait,":[66],"focusing":[67],"only":[68],"on":[69,159],"local":[70],"dependencies":[71],"domain.":[75],"To":[76],"tackle":[77],"limitations,":[79],"we":[80,95,127],"propose":[81],"an":[82],"innovative":[83],"three-streams":[84],"neural":[86],"network":[87],"model":[88],"PMF-GCN":[89,165],"(Posture-Movement-Frequency-enhanced":[90],"Graph":[91],"Convolutional":[92],"Network).":[93],"First,":[94],"introduce":[96],"first":[99],"time":[100],"recognition,":[104,172],"integration":[106],"from":[110,144],"translational":[111],"and":[112,123,148,173],"rotational":[113],"perspectives,":[114],"combined":[115],"with":[116],"frequency-domain":[117],"data,":[118],"capture":[120],"intensity":[122],"global":[124],"dependencies.":[125],"Then,":[126],"devise":[128],"novel":[130],"adaptive":[131],"feature":[132],"fusion":[133],"mechanism":[134],"(TR-AFM),":[135],"which":[136],"achieves":[137,166,174],"effective":[138],"extraction":[139],"spatial-temporal-specific":[141],"gaits":[145],"spatial":[149],"attention":[150],"mechanisms,":[151],"as":[152,154],"well":[153],"gated":[155],"units.":[156],"Comprehensive":[157],"experiments":[158],"two":[160],"public":[161],"datasets":[162],"show":[163],"leading":[167],"performance":[168],"state-of-the-art":[175],"performance.":[176]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-01-14T00:00:00"}
